TY - GEN
T1 - FICDF
T2 - 22nd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2024
AU - Ding, Shengli
AU - Han, Dong Jun
AU - Brinton, Christopher G.
AU - Dasala, Keerthi
N1 - Publisher Copyright:
© 2024 International Federation for Information Processing - IFIP.
PY - 2024
Y1 - 2024
N2 - Although Internet of Things (IoT) devices have been widely used, their simple structure restricts the deployment of advanced security protocols, making them vulnerable to cyber-attacks. Consequently, network administrators must adopt a zero-trust approach to identify each IoT communication entity. Recently, radio frequency (RF) or traffic data based fingerprinting has proven to be an effective IoT identification technique. Nevertheless, existing fingerprinting methods face limitations due to privacy concerns, the extreme non-independent distribution of fingerprint data, and the dynamic updating of IoT devices, hindering real-world deployment. We propose a Federated IoT Continuous Device Fingerprinting (FICDF) mechanism to address these challenges. In the traffic data preprocessing stage, we design a binary encoding and temporal tensor channel stacking mechanism to enhance the device-specific features in each training sample. Within the framework of federated incremental learning, we introduce a k-means multi-centroid exemplar-based Gaussian noise feature-sharing mechanism to simultaneously address the extreme non-IID nature of the data and the issue of catastrophic forgetting. To the best of our knowledge, this is the first study to tackle federated incremental device fingerprinting under extreme non-IID conditions. The code for this paper can be found at https://github.com/Squiding/FICDF_WiOpt2024
AB - Although Internet of Things (IoT) devices have been widely used, their simple structure restricts the deployment of advanced security protocols, making them vulnerable to cyber-attacks. Consequently, network administrators must adopt a zero-trust approach to identify each IoT communication entity. Recently, radio frequency (RF) or traffic data based fingerprinting has proven to be an effective IoT identification technique. Nevertheless, existing fingerprinting methods face limitations due to privacy concerns, the extreme non-independent distribution of fingerprint data, and the dynamic updating of IoT devices, hindering real-world deployment. We propose a Federated IoT Continuous Device Fingerprinting (FICDF) mechanism to address these challenges. In the traffic data preprocessing stage, we design a binary encoding and temporal tensor channel stacking mechanism to enhance the device-specific features in each training sample. Within the framework of federated incremental learning, we introduce a k-means multi-centroid exemplar-based Gaussian noise feature-sharing mechanism to simultaneously address the extreme non-IID nature of the data and the issue of catastrophic forgetting. To the best of our knowledge, this is the first study to tackle federated incremental device fingerprinting under extreme non-IID conditions. The code for this paper can be found at https://github.com/Squiding/FICDF_WiOpt2024
KW - Class incremental learning
KW - Federated learning
KW - IoT device fingerprinting
UR - http://www.scopus.com/inward/record.url?scp=85215513758&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85215513758&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85215513758
T3 - Proceedings of the International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt
SP - 337
EP - 344
BT - 2024 22nd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 October 2024 through 24 October 2024
ER -